A video based vehicle detection technology plays a key role in traffic video surveillance systems, which provides vehicle information for these monitoring systems. In this technology, the mainstream vehicle detection methods apply vehicle motion features to classify the video image pixels as either foreground or background pixels. Then, the foreground pixels are combined to locate vehicles. But these methods are not suitable for the situations that vehicles slowly move (i.e. lacking motion information of the vehicle). Apart from the motion based vehicle detection method, the image features such as contour, Texture and the like are utilized to locate and detect vehicles in many researches. However, many researchers generally use a single or few image features for vehicle detection, and for the detected vehicles they just locate the vehicles without illustrating information such as contour, texture and the like.
Hybrid image template consists of multiple image patches with different image features. According to the types of these image features, the image patches are categorized as sketch patch, texture patch, color patch and flatness patch. This template can describe in detail various object features in various positions of an image object when it is applied for detecting object. Using multiple features in this template for vehicle detection improves the detection accuracy for locating the object. In addition, each of image patches in this template can locally perturb their locations and orientations during vehicle detection, which makes this template deformable to adapt the object. Therefore, this invention applies the hybrid image template to vehicle localization and detailed description of vehicle features in a complex traffic scene.